1. Field of the Invention
Embodiments of the invention relate to steganography. Steganography is the art and science of secret communication, whose purpose is to hide the very presence of information in content such as images, video, and audio. Steganalysis is the art and science of detecting information hidden in content (e.g., images, video and audio) through the use of steganography. In particular, embodiments of the invention relate to the detection of hidden information in texture images.
2. Description of Background Art
The goals of steganalysis are to: (1) identify suspect content; (2) detect whether or mot the content has embedded information; and, (3) if possible, recover that information. Steganalysis is complicated primarily by four things: the suspect images may or may not have any information embedded into them in the first place; the information, if any, may have been encrypted before being embedded, into the content; some of the suspect content may have had noise or irrelevant data embedded into them (which reduces stealth, but cap make steganalysis very difficult); and unless you can completely recover, decrypt, and inspect the information, you often can't be sure whether you really have content that is being, used for transport of hidden information or not.
Images are one form of content in which information may be hidden. Since images are diverse and there are wide variations in data embedding approaches, effective methods for steganalysis can be particularly difficult to implement for images. However, since the cover medium, has been modified by the step of embedding, information, a cover image and an associated steganographic (stego) version of the cover image (i.e., the cover image with embedded information) generally will differ in some respect.
In particular, reliable steganalysis of texture images, which have characteristics that are different from smooth (i.e., non-texture) images, has proven difficult. Specifically, texture images have characteristics that are similar to noise. As a result; the embedded information is submerged in noise, and when methods for steganalysis are applied to texture images the embedded information is often very hard to detect with background art methods for steganalysis that were not developed with the characteristics of texture images in mind.
Our own background art methods for steganalysis, which are hereby incorporated by reference, are described in G. Xuan, Y. Q. Shi, J. Gao, D. Zou. C. Yang, Z. Zhang, P. Char, C. Chen, W. Chen, “Steganalysis. Based on Multiple Features Formed by Statistical Moments of Wavelet Characteristic Functions”, Information Hiding Workshop 2005, Barcelona, Spain, June 2005; and Y. Q. Shi, G. Xuan, D. Zou, J. Gao, C. Yang, Z. Zhang, P. Chai W. Chen, C. Chen, “Steganalysis Based on Moments of Characteristic Functions Using Wavelet Decomposition, Prediction-Error Image, and Neural Network”, IEEE international Conference on Multimedia & Expo 2005, Amsterdam, Netherlands, July, 2005 (hereinafter Shi et al.). Our background art methods perform well on standard smooth image datasets, say, CorelDraw™ image dataset. However, the performance of our background art methods deteriorates dramatically for texture images and thus, these background art methods are not satisfactory for steganalysis for texture image applications.
Other examples of background art that do not provide the desired level of performance for detection of information in texture images include: J. Fridrich, “Feature-based steganalysis for JPEG images and its implications for future design of steganographic schemes,” 6th Information Hiding Workshop, Toronto, ON, Canada, 2004 (hereinafter Fidrich); H. Farid, “Detecting hidden messages using higher-order statistical models”, International Conference on Image Processing, Rochester, N.Y., USA, 2002, (hereinafter Farid); C. Chen, Y. Q. Shi, W. Chen, “Statistical Moments Based Universal Steganalysis Using JPEG 2-D Array and 2-D Characteristic Function”, IEEE International Conference on Image Processing 2006, Atlanta, Ga., USA, Oct. 8-11, 2006 (hereinafter Chen et al); D. Zou, Y. Q. Shi, W. Su, G. Xuan, “Steganalysis based on Markov model of thresholded prediction-error image”, IEEE International Conference on Multimedia and Expo, Toronto, ON, Canada, Jul. 9-12, 2006 (hereinafter Zou et al.); and Y. Q. Shi, C. Chen, W. Chen, “A Markov Process Based Approach to Effective Attacking JPEG Steganography”, Information Hiding Workshop 2006, Old Town Alexandria, Va., USA, Jul. 10-12, 2006 (hereinafter Shi et al. II).
Additionally, recognition that background art methods for steganalysis have difficulty detecting information in texture images has also been reported in R. Bohme, “Assessment of Steganalytic Methods Using Multiple Regression Models”, Information Hiding Workshop 2005, Barcelona, Spain, June 2005 (hereinafter Bohme). In particular, Bohme found that images with noisy textures yield the least accurate detection results when two other exemplary background art methods for steganalysis: “Regular-Singular (RS)” and “Weighted Stego Image (WS)” are used. Therefore, there is clearly a need in the art for improved methods for steganalysis for texture images.